Need advice about which tool to choose?Ask the StackShare community!

Azure HDInsight

29
137
+ 1
0
Apache Spark

3K
3.5K
+ 1
140
Add tool

Apache Spark vs Azure HDInsight: What are the differences?

  1. Distribution and Scalability: Apache Spark is a distributed processing system that allows users to process large-scale datasets in parallel. It is designed to be highly scalable and can handle workloads on clusters of thousands of nodes. Azure HDInsight, on the other hand, is a fully-managed cloud service that provides Apache Hadoop, Spark, and other big data processing frameworks. It leverages the scalability of the Azure cloud platform to handle large-scale data processing tasks.

  2. Ease of Use and Flexibility: Spark provides a user-friendly API that allows developers to write applications in multiple languages such as Scala, Java, Python, and R. It offers a rich set of libraries and tools for data analytics, machine learning, and graph processing. Azure HDInsight, being a managed service, simplifies the deployment and management of Spark clusters. It integrates well with other Azure services and provides an intuitive user interface for managing and monitoring Spark jobs.

  3. Integration with Azure Services: HDInsight provides tight integration with other Azure services such as Azure Storage, Azure Data Lake Storage, Azure Active Directory, and Azure SQL Database. This enables users to easily ingest, store, and analyze data from various sources within the Azure ecosystem. Spark can seamlessly read and write data to/from these Azure services, making it easier to build end-to-end data pipelines.

  4. Advanced Analytics and Machine Learning: Spark has built-in support for advanced analytics and machine learning through its MLlib library. It provides a wide range of algorithms for classification, regression, clustering, and recommendation. Azure HDInsight extends Spark's machine learning capabilities by integrating with other Azure services such as Azure Machine Learning and Azure Databricks. This allows users to leverage the power of these services for building and deploying advanced ML models at scale.

  5. Security and Compliance: HDInsight provides robust security features such as role-based access control (RBAC), Azure Active Directory integration, network isolation, and encryption at rest. It also helps organizations meet compliance requirements by supporting data governance frameworks like GDPR, HIPAA, and ISO 27001. Spark, on the other hand, provides fine-grained security controls through features like authentication, authorization, and encryption. It can integrate with external systems for user authentication and access control.

  6. Pricing and Cost Optimization: Apache Spark is an open-source framework and can be used for free. However, the cost of deploying, configuring, and managing Spark clusters can add up for organizations. Azure HDInsight provides a pay-as-you-go pricing model, allowing users to optimize costs by scaling clusters up or down based on workload demands. It also offers cost management features like automatic scaling, cluster resizing, and instance type selection to ensure efficient resource utilization.

In Summary, Apache Spark and Azure HDInsight differ in terms of distribution and scalability, ease of use and flexibility, integration with Azure services, advanced analytics and machine learning capabilities, security and compliance features, and pricing and cost optimization.

Advice on Azure HDInsight and Apache Spark
Nilesh Akhade
Technical Architect at Self Employed · | 5 upvotes · 514.9K views

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

See more
Replies (2)
Recommends
on
ElasticsearchElasticsearch

The first solution that came to me is to use upsert to update ElasticSearch:

  1. Use the primary-key as ES document id
  2. Upsert the records to ES as soon as you receive them. As you are using upsert, the 2nd record of the same primary-key will not overwrite the 1st one, but will be merged with it.

Cons: The load on ES will be higher, due to upsert.

To use Flink:

  1. Create a KeyedDataStream by the primary-key
  2. In the ProcessFunction, save the first record in a State. At the same time, create a Timer for 15 minutes in the future
  3. When the 2nd record comes, read the 1st record from the State, merge those two, and send out the result, and clear the State and the Timer if it has not fired
  4. When the Timer fires, read the 1st record from the State and send out as the output record.
  5. Have a 2nd Timer of 6 hours (or more) if you are not using Windowing to clean up the State

Pro: if you have already having Flink ingesting this stream. Otherwise, I would just go with the 1st solution.

See more
Akshaya Rawat
Senior Specialist Platform at Publicis Sapient · | 3 upvotes · 359.8K views
Recommends
on
Apache SparkApache Spark

Please refer "Structured Streaming" feature of Spark. Refer "Stream - Stream Join" at https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#stream-stream-joins . In short you need to specify "Define watermark delays on both inputs" and "Define a constraint on time across the two inputs"

See more
Get Advice from developers at your company using StackShare Enterprise. Sign up for StackShare Enterprise.
Learn More
Pros of Azure HDInsight
Pros of Apache Spark
    Be the first to leave a pro
    • 61
      Open-source
    • 48
      Fast and Flexible
    • 8
      One platform for every big data problem
    • 8
      Great for distributed SQL like applications
    • 6
      Easy to install and to use
    • 3
      Works well for most Datascience usecases
    • 2
      Interactive Query
    • 2
      Machine learning libratimery, Streaming in real
    • 2
      In memory Computation

    Sign up to add or upvote prosMake informed product decisions

    Cons of Azure HDInsight
    Cons of Apache Spark
      Be the first to leave a con
      • 4
        Speed

      Sign up to add or upvote consMake informed product decisions

      - No public GitHub repository available -

      What is Azure HDInsight?

      It is a cloud-based service from Microsoft for big data analytics that helps organizations process large amounts of streaming or historical data.

      What is Apache Spark?

      Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

      Need advice about which tool to choose?Ask the StackShare community!

      Jobs that mention Azure HDInsight and Apache Spark as a desired skillset
      What companies use Azure HDInsight?
      What companies use Apache Spark?
      See which teams inside your own company are using Azure HDInsight or Apache Spark.
      Sign up for StackShare EnterpriseLearn More

      Sign up to get full access to all the companiesMake informed product decisions

      What tools integrate with Azure HDInsight?
      What tools integrate with Apache Spark?

      Sign up to get full access to all the tool integrationsMake informed product decisions

      Blog Posts

      Mar 24 2021 at 12:57PM

      Pinterest

      GitJenkinsKafka+7
      3
      2124
      MySQLKafkaApache Spark+6
      2
      1999
      Aug 28 2019 at 3:10AM

      Segment

      PythonJavaAmazon S3+16
      7
      2551
      What are some alternatives to Azure HDInsight and Apache Spark?
      Amazon EMR
      It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.
      Azure Databricks
      Accelerate big data analytics and artificial intelligence (AI) solutions with Azure Databricks, a fast, easy and collaborative Apache Spark–based analytics service.
      Hadoop
      The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
      Azure Machine Learning
      Azure Machine Learning is a fully-managed cloud service that enables data scientists and developers to efficiently embed predictive analytics into their applications, helping organizations use massive data sets and bring all the benefits of the cloud to machine learning.
      Azure Data Factory
      It is a service designed to allow developers to integrate disparate data sources. It is a platform somewhat like SSIS in the cloud to manage the data you have both on-prem and in the cloud.
      See all alternatives